Overview

Dataset statistics

Number of variables31
Number of observations569338
Missing cells2936921
Missing cells (%)16.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory134.7 MiB
Average record size in memory248.0 B

Variable types

Numeric20
DateTime1
Categorical4
Text6

Alerts

CANCELLED is highly imbalanced (90.4%)Imbalance
DIVERTED is highly imbalanced (97.5%)Imbalance
DEP_TIME has 6617 (1.2%) missing valuesMissing
DEP_DELAY has 6618 (1.2%) missing valuesMissing
TAXI_OUT has 7018 (1.2%) missing valuesMissing
TAXI_IN has 7146 (1.3%) missing valuesMissing
ARR_TIME has 7145 (1.3%) missing valuesMissing
ARR_DELAY has 8451 (1.5%) missing valuesMissing
CANCELLATION_CODE has 562280 (98.8%) missing valuesMissing
AIR_TIME has 8451 (1.5%) missing valuesMissing
CARRIER_DELAY has 464639 (81.6%) missing valuesMissing
WEATHER_DELAY has 464639 (81.6%) missing valuesMissing
NAS_DELAY has 464639 (81.6%) missing valuesMissing
SECURITY_DELAY has 464639 (81.6%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 464639 (81.6%) missing valuesMissing
SECURITY_DELAY is highly skewed (γ1 = 89.29427867)Skewed
DEP_DELAY has 25841 (4.5%) zerosZeros
ARR_DELAY has 10503 (1.8%) zerosZeros
CARRIER_DELAY has 48222 (8.5%) zerosZeros
WEATHER_DELAY has 99278 (17.4%) zerosZeros
NAS_DELAY has 54825 (9.6%) zerosZeros
SECURITY_DELAY has 104179 (18.3%) zerosZeros
LATE_AIRCRAFT_DELAY has 52932 (9.3%) zerosZeros

Reproduction

Analysis started2024-03-30 05:53:18.293431
Analysis finished2024-03-30 05:56:06.553725
Duration2 minutes and 48.26 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0660434
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:06.692832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9729996
Coefficient of variation (CV)0.4852382
Kurtosis-1.2055722
Mean4.0660434
Median Absolute Deviation (MAD)2
Skewness-0.096568792
Sum2314953
Variance3.8927273
MonotonicityIncreasing
2024-03-30T02:56:07.009037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 100284
17.6%
6 84214
14.8%
4 80529
14.1%
1 80423
14.1%
7 76803
13.5%
3 73919
13.0%
2 73166
12.9%
ValueCountFrequency (%)
1 80423
14.1%
2 73166
12.9%
3 73919
13.0%
4 80529
14.1%
5 100284
17.6%
6 84214
14.8%
7 76803
13.5%
ValueCountFrequency (%)
7 76803
13.5%
6 84214
14.8%
5 100284
17.6%
4 80529
14.1%
3 73919
13.0%
2 73166
12.9%
1 80423
14.1%
Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Minimum2023-09-01 00:00:00
Maximum2023-09-30 00:00:00
2024-03-30T02:56:07.308327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:56:07.675573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
WN
117870 
DL
81701 
AA
76972 
UA
62591 
OO
59245 
Other values (10)
170959 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1138676
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 117870
20.7%
DL 81701
14.4%
AA 76972
13.5%
UA 62591
11.0%
OO 59245
10.4%
YX 24839
 
4.4%
AS 21426
 
3.8%
B6 21412
 
3.8%
NK 21036
 
3.7%
MQ 18789
 
3.3%
Other values (5) 63457
11.1%

Length

2024-03-30T02:56:08.228071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 117870
20.7%
dl 81701
14.4%
aa 76972
13.5%
ua 62591
11.0%
oo 59245
10.4%
yx 24839
 
4.4%
as 21426
 
3.8%
b6 21412
 
3.8%
nk 21036
 
3.7%
mq 18789
 
3.3%
Other values (5) 63457
11.1%

Most occurring characters

ValueCountFrequency (%)
A 244679
21.5%
N 138906
12.2%
O 134665
11.8%
W 117870
10.4%
D 81701
 
7.2%
L 81701
 
7.2%
U 62591
 
5.5%
9 33672
 
3.0%
Y 24839
 
2.2%
X 24839
 
2.2%
Other values (11) 193213
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1138676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 244679
21.5%
N 138906
12.2%
O 134665
11.8%
W 117870
10.4%
D 81701
 
7.2%
L 81701
 
7.2%
U 62591
 
5.5%
9 33672
 
3.0%
Y 24839
 
2.2%
X 24839
 
2.2%
Other values (11) 193213
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1138676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 244679
21.5%
N 138906
12.2%
O 134665
11.8%
W 117870
10.4%
D 81701
 
7.2%
L 81701
 
7.2%
U 62591
 
5.5%
9 33672
 
3.0%
Y 24839
 
2.2%
X 24839
 
2.2%
Other values (11) 193213
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1138676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 244679
21.5%
N 138906
12.2%
O 134665
11.8%
W 117870
10.4%
D 81701
 
7.2%
L 81701
 
7.2%
U 62591
 
5.5%
9 33672
 
3.0%
Y 24839
 
2.2%
X 24839
 
2.2%
Other values (11) 193213
17.0%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5936
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2353.8195
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:08.566287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile294.85
Q11081
median2117
Q33437
95-th percentile5386
Maximum8819
Range8818
Interquartile range (IQR)2356

Descriptive statistics

Standard deviation1575.1363
Coefficient of variation (CV)0.66918311
Kurtosis-0.66830596
Mean2353.8195
Median Absolute Deviation (MAD)1140
Skewness0.55299996
Sum1.3401189 × 109
Variance2481054.3
MonotonicityNot monotonic
2024-03-30T02:56:08.952293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1167 294
 
0.1%
321 284
 
< 0.1%
555 283
 
< 0.1%
398 277
 
< 0.1%
540 275
 
< 0.1%
656 275
 
< 0.1%
1168 274
 
< 0.1%
383 270
 
< 0.1%
2080 269
 
< 0.1%
1008 267
 
< 0.1%
Other values (5926) 566570
99.5%
ValueCountFrequency (%)
1 208
< 0.1%
2 173
< 0.1%
3 93
< 0.1%
4 161
< 0.1%
5 90
< 0.1%
6 86
 
< 0.1%
7 158
< 0.1%
8 75
 
< 0.1%
9 160
< 0.1%
10 217
< 0.1%
ValueCountFrequency (%)
8819 1
 
< 0.1%
8801 1
 
< 0.1%
8800 3
< 0.1%
8792 2
< 0.1%
8791 1
 
< 0.1%
8790 2
< 0.1%
8789 1
 
< 0.1%
8788 1
 
< 0.1%
8787 2
< 0.1%
8786 2
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12636.878
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:09.350335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1533.8691
Coefficient of variation (CV)0.12138038
Kurtosis-1.3056074
Mean12636.878
Median Absolute Deviation (MAD)1591
Skewness0.11709455
Sum7.194655 × 109
Variance2352754.3
MonotonicityNot monotonic
2024-03-30T02:56:09.762778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28344
 
5.0%
11292 24738
 
4.3%
11298 24533
 
4.3%
13930 22384
 
3.9%
11057 16175
 
2.8%
12892 16152
 
2.8%
12889 16110
 
2.8%
14747 15041
 
2.6%
14107 14285
 
2.5%
12953 13372
 
2.3%
Other values (330) 378204
66.4%
ValueCountFrequency (%)
10135 376
 
0.1%
10136 118
 
< 0.1%
10140 2131
0.4%
10141 60
 
< 0.1%
10146 60
 
< 0.1%
10154 231
 
< 0.1%
10155 84
 
< 0.1%
10157 142
 
< 0.1%
10158 249
 
< 0.1%
10165 9
 
< 0.1%
ValueCountFrequency (%)
16869 150
 
< 0.1%
16218 148
 
< 0.1%
15991 60
 
< 0.1%
15919 1017
0.2%
15897 60
 
< 0.1%
15841 60
 
< 0.1%
15624 741
0.1%
15607 60
 
< 0.1%
15582 51
 
< 0.1%
15569 51
 
< 0.1%

ORIGIN
Text

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:10.611434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1708014
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBNA
2nd rowLGA
3rd rowRIC
4th rowCLT
5th rowJFK
ValueCountFrequency (%)
atl 28344
 
5.0%
den 24738
 
4.3%
dfw 24533
 
4.3%
ord 22384
 
3.9%
clt 16175
 
2.8%
lax 16152
 
2.8%
las 16110
 
2.8%
sea 15041
 
2.6%
phx 14285
 
2.5%
lga 13372
 
2.3%
Other values (330) 378204
66.4%
2024-03-30T02:56:11.571082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 195455
 
11.4%
L 157746
 
9.2%
S 146077
 
8.6%
D 137148
 
8.0%
T 90340
 
5.3%
O 87035
 
5.1%
C 85620
 
5.0%
M 74652
 
4.4%
F 71066
 
4.2%
E 67731
 
4.0%
Other values (16) 595144
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 195455
 
11.4%
L 157746
 
9.2%
S 146077
 
8.6%
D 137148
 
8.0%
T 90340
 
5.3%
O 87035
 
5.1%
C 85620
 
5.0%
M 74652
 
4.4%
F 71066
 
4.2%
E 67731
 
4.0%
Other values (16) 595144
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 195455
 
11.4%
L 157746
 
9.2%
S 146077
 
8.6%
D 137148
 
8.0%
T 90340
 
5.3%
O 87035
 
5.1%
C 85620
 
5.0%
M 74652
 
4.4%
F 71066
 
4.2%
E 67731
 
4.0%
Other values (16) 595144
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 195455
 
11.4%
L 157746
 
9.2%
S 146077
 
8.6%
D 137148
 
8.0%
T 90340
 
5.3%
O 87035
 
5.1%
C 85620
 
5.0%
M 74652
 
4.4%
F 71066
 
4.2%
E 67731
 
4.0%
Other values (16) 595144
34.8%
Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:12.179777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.039801
Min length8

Characters and Unicode

Total characters7424054
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNashville, TN
2nd rowNew York, NY
3rd rowRichmond, VA
4th rowCharlotte, NC
5th rowNew York, NY
ValueCountFrequency (%)
ca 61847
 
4.7%
tx 60810
 
4.6%
fl 43758
 
3.3%
ny 30676
 
2.3%
il 30379
 
2.3%
ga 30297
 
2.3%
san 29589
 
2.2%
chicago 29493
 
2.2%
atlanta 28344
 
2.1%
new 27749
 
2.1%
Other values (407) 950846
71.8%
2024-03-30T02:56:13.054870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
754450
 
10.2%
, 569338
 
7.7%
a 565926
 
7.6%
o 409299
 
5.5%
e 392692
 
5.3%
n 363301
 
4.9%
t 357737
 
4.8%
l 330201
 
4.4%
i 284039
 
3.8%
r 265682
 
3.6%
Other values (47) 3131389
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7424054
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
754450
 
10.2%
, 569338
 
7.7%
a 565926
 
7.6%
o 409299
 
5.5%
e 392692
 
5.3%
n 363301
 
4.9%
t 357737
 
4.8%
l 330201
 
4.4%
i 284039
 
3.8%
r 265682
 
3.6%
Other values (47) 3131389
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7424054
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
754450
 
10.2%
, 569338
 
7.7%
a 565926
 
7.6%
o 409299
 
5.5%
e 392692
 
5.3%
n 363301
 
4.9%
t 357737
 
4.8%
l 330201
 
4.4%
i 284039
 
3.8%
r 265682
 
3.6%
Other values (47) 3131389
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7424054
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
754450
 
10.2%
, 569338
 
7.7%
a 565926
 
7.6%
o 409299
 
5.5%
e 392692
 
5.3%
n 363301
 
4.9%
t 357737
 
4.8%
l 330201
 
4.4%
i 284039
 
3.8%
r 265682
 
3.6%
Other values (47) 3131389
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:13.463840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1698288
Min length4

Characters and Unicode

Total characters4651394
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTennessee
2nd rowNew York
3rd rowVirginia
4th rowNorth Carolina
5th rowNew York
ValueCountFrequency (%)
california 61847
 
9.5%
texas 60810
 
9.3%
new 45391
 
7.0%
florida 43758
 
6.7%
york 30676
 
4.7%
illinois 30379
 
4.7%
georgia 30297
 
4.6%
carolina 29513
 
4.5%
colorado 27332
 
4.2%
north 25651
 
3.9%
Other values (51) 266052
40.8%
2024-03-30T02:56:14.168466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 624851
13.4%
i 520822
 
11.2%
o 443414
 
9.5%
n 351144
 
7.5%
r 330787
 
7.1%
e 288209
 
6.2%
s 270558
 
5.8%
l 254909
 
5.5%
C 120424
 
2.6%
t 111963
 
2.4%
Other values (37) 1334313
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4651394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 624851
13.4%
i 520822
 
11.2%
o 443414
 
9.5%
n 351144
 
7.5%
r 330787
 
7.1%
e 288209
 
6.2%
s 270558
 
5.8%
l 254909
 
5.5%
C 120424
 
2.6%
t 111963
 
2.4%
Other values (37) 1334313
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4651394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 624851
13.4%
i 520822
 
11.2%
o 443414
 
9.5%
n 351144
 
7.5%
r 330787
 
7.1%
e 288209
 
6.2%
s 270558
 
5.8%
l 254909
 
5.5%
C 120424
 
2.6%
t 111963
 
2.4%
Other values (37) 1334313
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4651394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 624851
13.4%
i 520822
 
11.2%
o 443414
 
9.5%
n 351144
 
7.5%
r 330787
 
7.1%
e 288209
 
6.2%
s 270558
 
5.8%
l 254909
 
5.5%
C 120424
 
2.6%
t 111963
 
2.4%
Other values (37) 1334313
28.7%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.990457
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:14.546111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median51
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.783593
Coefficient of variation (CV)0.48705893
Kurtosis-1.3208812
Mean54.990457
Median Absolute Deviation (MAD)23
Skewness-0.050075234
Sum31308157
Variance717.36086
MonotonicityNot monotonic
2024-03-30T02:56:14.884880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 61847
 
10.9%
74 60810
 
10.7%
33 43758
 
7.7%
22 30676
 
5.4%
41 30379
 
5.3%
34 30297
 
5.3%
82 27332
 
4.8%
36 24293
 
4.3%
38 19832
 
3.5%
85 17793
 
3.1%
Other values (42) 222321
39.0%
ValueCountFrequency (%)
1 3191
 
0.6%
2 10538
1.9%
3 2474
 
0.4%
4 257
 
< 0.1%
5 100
 
< 0.1%
11 1732
 
0.3%
12 1728
 
0.3%
13 12228
2.1%
14 544
 
0.1%
15 1235
 
0.2%
ValueCountFrequency (%)
93 17416
 
3.1%
92 6461
 
1.1%
91 61847
10.9%
88 839
 
0.1%
87 9707
 
1.7%
86 2364
 
0.4%
85 17793
 
3.1%
84 2248
 
0.4%
83 2365
 
0.4%
82 27332
4.8%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12636.993
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:15.211275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1533.9335
Coefficient of variation (CV)0.12138438
Kurtosis-1.3057584
Mean12636.993
Median Absolute Deviation (MAD)1591
Skewness0.11690032
Sum7.1947203 × 109
Variance2352952.1
MonotonicityNot monotonic
2024-03-30T02:56:15.595184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28359
 
5.0%
11292 24727
 
4.3%
11298 24531
 
4.3%
13930 22368
 
3.9%
11057 16168
 
2.8%
12892 16152
 
2.8%
12889 16130
 
2.8%
14747 15057
 
2.6%
14107 14281
 
2.5%
12953 13371
 
2.3%
Other values (330) 378194
66.4%
ValueCountFrequency (%)
10135 377
 
0.1%
10136 118
 
< 0.1%
10140 2136
0.4%
10141 60
 
< 0.1%
10146 60
 
< 0.1%
10154 231
 
< 0.1%
10155 84
 
< 0.1%
10157 142
 
< 0.1%
10158 249
 
< 0.1%
10165 9
 
< 0.1%
ValueCountFrequency (%)
16869 150
 
< 0.1%
16218 147
 
< 0.1%
15991 60
 
< 0.1%
15919 1016
0.2%
15897 60
 
< 0.1%
15841 60
 
< 0.1%
15624 741
0.1%
15607 60
 
< 0.1%
15582 51
 
< 0.1%
15569 51
 
< 0.1%

DEST
Text

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:16.289451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1708014
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowLGA
2nd rowBNA
3rd rowMSP
4th rowJFK
5th rowCLT
ValueCountFrequency (%)
atl 28359
 
5.0%
den 24727
 
4.3%
dfw 24531
 
4.3%
ord 22368
 
3.9%
clt 16168
 
2.8%
lax 16152
 
2.8%
las 16130
 
2.8%
sea 15057
 
2.6%
phx 14281
 
2.5%
lga 13371
 
2.3%
Other values (330) 378194
66.4%
2024-03-30T02:56:17.225686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 195473
 
11.4%
L 157775
 
9.2%
S 146122
 
8.6%
D 137121
 
8.0%
T 90352
 
5.3%
O 87039
 
5.1%
C 85591
 
5.0%
M 74640
 
4.4%
F 71087
 
4.2%
E 67732
 
4.0%
Other values (16) 595082
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 195473
 
11.4%
L 157775
 
9.2%
S 146122
 
8.6%
D 137121
 
8.0%
T 90352
 
5.3%
O 87039
 
5.1%
C 85591
 
5.0%
M 74640
 
4.4%
F 71087
 
4.2%
E 67732
 
4.0%
Other values (16) 595082
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 195473
 
11.4%
L 157775
 
9.2%
S 146122
 
8.6%
D 137121
 
8.0%
T 90352
 
5.3%
O 87039
 
5.1%
C 85591
 
5.0%
M 74640
 
4.4%
F 71087
 
4.2%
E 67732
 
4.0%
Other values (16) 595082
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 195473
 
11.4%
L 157775
 
9.2%
S 146122
 
8.6%
D 137121
 
8.0%
T 90352
 
5.3%
O 87039
 
5.1%
C 85591
 
5.0%
M 74640
 
4.4%
F 71087
 
4.2%
E 67732
 
4.0%
Other values (16) 595082
34.8%
Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:17.990553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.040436
Min length8

Characters and Unicode

Total characters7424416
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNew York, NY
2nd rowNashville, TN
3rd rowMinneapolis, MN
4th rowNew York, NY
5th rowCharlotte, NC
ValueCountFrequency (%)
ca 61865
 
4.7%
tx 60790
 
4.6%
fl 43743
 
3.3%
ny 30681
 
2.3%
il 30356
 
2.3%
ga 30312
 
2.3%
san 29597
 
2.2%
chicago 29471
 
2.2%
atlanta 28359
 
2.1%
new 27751
 
2.1%
Other values (407) 950910
71.8%
2024-03-30T02:56:18.994819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
754497
 
10.2%
, 569338
 
7.7%
a 565980
 
7.6%
o 409292
 
5.5%
e 392742
 
5.3%
n 363304
 
4.9%
t 357778
 
4.8%
l 330251
 
4.4%
i 284036
 
3.8%
r 265684
 
3.6%
Other values (47) 3131514
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7424416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
754497
 
10.2%
, 569338
 
7.7%
a 565980
 
7.6%
o 409292
 
5.5%
e 392742
 
5.3%
n 363304
 
4.9%
t 357778
 
4.8%
l 330251
 
4.4%
i 284036
 
3.8%
r 265684
 
3.6%
Other values (47) 3131514
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7424416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
754497
 
10.2%
, 569338
 
7.7%
a 565980
 
7.6%
o 409292
 
5.5%
e 392742
 
5.3%
n 363304
 
4.9%
t 357778
 
4.8%
l 330251
 
4.4%
i 284036
 
3.8%
r 265684
 
3.6%
Other values (47) 3131514
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7424416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
754497
 
10.2%
, 569338
 
7.7%
a 565980
 
7.6%
o 409292
 
5.5%
e 392742
 
5.3%
n 363304
 
4.9%
t 357778
 
4.8%
l 330251
 
4.4%
i 284036
 
3.8%
r 265684
 
3.6%
Other values (47) 3131514
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:19.473242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.169755
Min length4

Characters and Unicode

Total characters4651352
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowTennessee
3rd rowMinnesota
4th rowNew York
5th rowNorth Carolina
ValueCountFrequency (%)
california 61865
 
9.5%
texas 60790
 
9.3%
new 45396
 
7.0%
florida 43743
 
6.7%
york 30681
 
4.7%
illinois 30356
 
4.7%
georgia 30312
 
4.7%
carolina 29506
 
4.5%
colorado 27317
 
4.2%
north 25650
 
3.9%
Other values (51) 266078
40.8%
2024-03-30T02:56:20.264100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 624907
13.4%
i 520819
 
11.2%
o 443382
 
9.5%
n 351161
 
7.5%
r 330775
 
7.1%
e 288213
 
6.2%
s 270525
 
5.8%
l 254839
 
5.5%
C 120421
 
2.6%
t 111964
 
2.4%
Other values (37) 1334346
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4651352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 624907
13.4%
i 520819
 
11.2%
o 443382
 
9.5%
n 351161
 
7.5%
r 330775
 
7.1%
e 288213
 
6.2%
s 270525
 
5.8%
l 254839
 
5.5%
C 120421
 
2.6%
t 111964
 
2.4%
Other values (37) 1334346
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4651352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 624907
13.4%
i 520819
 
11.2%
o 443382
 
9.5%
n 351161
 
7.5%
r 330775
 
7.1%
e 288213
 
6.2%
s 270525
 
5.8%
l 254839
 
5.5%
C 120421
 
2.6%
t 111964
 
2.4%
Other values (37) 1334346
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4651352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 624907
13.4%
i 520819
 
11.2%
o 443382
 
9.5%
n 351161
 
7.5%
r 330775
 
7.1%
e 288213
 
6.2%
s 270525
 
5.8%
l 254839
 
5.5%
C 120421
 
2.6%
t 111964
 
2.4%
Other values (37) 1334346
28.7%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.996299
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:20.660925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median51
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.783854
Coefficient of variation (CV)0.48701193
Kurtosis-1.321044
Mean54.996299
Median Absolute Deviation (MAD)23
Skewness-0.050254634
Sum31311483
Variance717.37484
MonotonicityNot monotonic
2024-03-30T02:56:21.049939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 61865
 
10.9%
74 60790
 
10.7%
33 43743
 
7.7%
22 30681
 
5.4%
41 30356
 
5.3%
34 30312
 
5.3%
82 27317
 
4.8%
36 24292
 
4.3%
38 19828
 
3.5%
85 17815
 
3.1%
Other values (42) 222339
39.1%
ValueCountFrequency (%)
1 3184
 
0.6%
2 10540
1.9%
3 2462
 
0.4%
4 257
 
< 0.1%
5 100
 
< 0.1%
11 1733
 
0.3%
12 1731
 
0.3%
13 12225
2.1%
14 544
 
0.1%
15 1236
 
0.2%
ValueCountFrequency (%)
93 17435
 
3.1%
92 6469
 
1.1%
91 61865
10.9%
88 835
 
0.1%
87 9707
 
1.7%
86 2369
 
0.4%
85 17815
 
3.1%
84 2246
 
0.4%
83 2369
 
0.4%
82 27317
4.8%

CRS_DEP_TIME
Real number (ℝ)

Distinct1210
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1327.5619
Minimum4
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:21.418008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile605
Q1910
median1319
Q31734
95-th percentile2120
Maximum2359
Range2355
Interquartile range (IQR)824

Descriptive statistics

Standard deviation487.11931
Coefficient of variation (CV)0.36692777
Kurtosis-1.0624147
Mean1327.5619
Median Absolute Deviation (MAD)411
Skewness0.096408495
Sum7.5583142 × 108
Variance237285.23
MonotonicityNot monotonic
2024-03-30T02:56:21.854041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 11487
 
2.0%
700 8559
 
1.5%
800 5275
 
0.9%
630 3617
 
0.6%
900 3594
 
0.6%
830 3489
 
0.6%
1000 3471
 
0.6%
1100 3183
 
0.6%
730 3088
 
0.5%
710 2936
 
0.5%
Other values (1200) 520639
91.4%
ValueCountFrequency (%)
4 3
 
< 0.1%
5 26
 
< 0.1%
10 13
 
< 0.1%
15 66
< 0.1%
20 2
 
< 0.1%
21 5
 
< 0.1%
22 3
 
< 0.1%
24 9
 
< 0.1%
25 7
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
2359 722
0.1%
2358 63
 
< 0.1%
2357 83
 
< 0.1%
2356 31
 
< 0.1%
2355 277
 
< 0.1%
2354 61
 
< 0.1%
2353 7
 
< 0.1%
2352 90
 
< 0.1%
2351 8
 
< 0.1%
2350 112
 
< 0.1%

DEP_TIME
Real number (ℝ)

MISSING 

Distinct1413
Distinct (%)0.3%
Missing6617
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1329.0055
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:22.219143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile601
Q1911
median1320
Q31741
95-th percentile2134
Maximum2400
Range2399
Interquartile range (IQR)830

Descriptive statistics

Standard deviation501.71604
Coefficient of variation (CV)0.3775124
Kurtosis-0.9925861
Mean1329.0055
Median Absolute Deviation (MAD)415
Skewness0.054983195
Sum7.4785928 × 108
Variance251718.98
MonotonicityNot monotonic
2024-03-30T02:56:22.556388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1507
 
0.3%
557 1260
 
0.2%
556 1233
 
0.2%
558 1219
 
0.2%
554 1211
 
0.2%
655 1184
 
0.2%
559 1158
 
0.2%
654 1135
 
0.2%
553 1115
 
0.2%
656 1073
 
0.2%
Other values (1403) 550626
96.7%
(Missing) 6617
 
1.2%
ValueCountFrequency (%)
1 79
< 0.1%
2 53
< 0.1%
3 47
< 0.1%
4 50
< 0.1%
5 48
< 0.1%
6 56
< 0.1%
7 42
< 0.1%
8 48
< 0.1%
9 37
< 0.1%
10 40
< 0.1%
ValueCountFrequency (%)
2400 40
 
< 0.1%
2359 75
< 0.1%
2358 83
< 0.1%
2357 84
< 0.1%
2356 90
< 0.1%
2355 116
< 0.1%
2354 109
< 0.1%
2353 115
< 0.1%
2352 139
< 0.1%
2351 120
< 0.1%

DEP_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1139
Distinct (%)0.2%
Missing6618
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean10.880909
Minimum-42
Maximum2360
Zeros25841
Zeros (%)4.5%
Negative338647
Negative (%)59.5%
Memory size4.3 MiB
2024-03-30T02:56:22.902404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-42
5-th percentile-10
Q1-6
median-2
Q37
95-th percentile73
Maximum2360
Range2402
Interquartile range (IQR)13

Descriptive statistics

Standard deviation53.844126
Coefficient of variation (CV)4.9484953
Kurtosis211.97886
Mean10.880909
Median Absolute Deviation (MAD)5
Skewness11.323317
Sum6122905
Variance2899.1899
MonotonicityNot monotonic
2024-03-30T02:56:23.223079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 44214
 
7.8%
-4 40368
 
7.1%
-3 38444
 
6.8%
-6 35833
 
6.3%
-2 34387
 
6.0%
-1 30687
 
5.4%
-7 30681
 
5.4%
0 25841
 
4.5%
-8 24909
 
4.4%
-9 18767
 
3.3%
Other values (1129) 238589
41.9%
ValueCountFrequency (%)
-42 1
 
< 0.1%
-38 1
 
< 0.1%
-37 1
 
< 0.1%
-33 3
 
< 0.1%
-32 5
 
< 0.1%
-31 2
 
< 0.1%
-30 2
 
< 0.1%
-29 6
 
< 0.1%
-28 15
< 0.1%
-27 14
< 0.1%
ValueCountFrequency (%)
2360 1
< 0.1%
2329 1
< 0.1%
2304 1
< 0.1%
2293 1
< 0.1%
2218 1
< 0.1%
2027 1
< 0.1%
1789 1
< 0.1%
1761 1
< 0.1%
1701 1
< 0.1%
1691 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct176
Distinct (%)< 0.1%
Missing7018
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean17.490032
Minimum1
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:23.535763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile33
Maximum182
Range181
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.4663944
Coefficient of variation (CV)0.5412451
Kurtosis30.626669
Mean17.490032
Median Absolute Deviation (MAD)4
Skewness3.9024438
Sum9834995
Variance89.612622
MonotonicityNot monotonic
2024-03-30T02:56:24.030483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 46044
 
8.1%
13 45664
 
8.0%
14 43518
 
7.6%
11 42164
 
7.4%
15 39184
 
6.9%
16 34947
 
6.1%
10 33745
 
5.9%
17 30455
 
5.3%
18 25915
 
4.6%
9 23110
 
4.1%
Other values (166) 197574
34.7%
ValueCountFrequency (%)
1 9
 
< 0.1%
2 12
 
< 0.1%
3 56
 
< 0.1%
4 197
 
< 0.1%
5 489
 
0.1%
6 2158
 
0.4%
7 6059
 
1.1%
8 12829
 
2.3%
9 23110
4.1%
10 33745
5.9%
ValueCountFrequency (%)
182 1
 
< 0.1%
180 1
 
< 0.1%
177 1
 
< 0.1%
176 2
< 0.1%
175 1
 
< 0.1%
173 2
< 0.1%
172 1
 
< 0.1%
170 1
 
< 0.1%
169 4
< 0.1%
168 1
 
< 0.1%

TAXI_IN
Real number (ℝ)

MISSING 

Distinct147
Distinct (%)< 0.1%
Missing7146
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean8.1845419
Minimum1
Maximum204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:24.406283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q310
95-th percentile19
Maximum204
Range203
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.4990072
Coefficient of variation (CV)0.79405875
Kurtosis44.770239
Mean8.1845419
Median Absolute Deviation (MAD)2
Skewness4.5736456
Sum4601284
Variance42.237094
MonotonicityNot monotonic
2024-03-30T02:56:24.752704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 80866
14.2%
5 79036
13.9%
6 65830
11.6%
7 52712
9.3%
3 49271
8.7%
8 41657
7.3%
9 33506
 
5.9%
10 26736
 
4.7%
11 21034
 
3.7%
12 16648
 
2.9%
Other values (137) 94896
16.7%
ValueCountFrequency (%)
1 791
 
0.1%
2 12273
 
2.2%
3 49271
8.7%
4 80866
14.2%
5 79036
13.9%
6 65830
11.6%
7 52712
9.3%
8 41657
7.3%
9 33506
5.9%
10 26736
 
4.7%
ValueCountFrequency (%)
204 1
< 0.1%
194 1
< 0.1%
167 2
< 0.1%
166 2
< 0.1%
164 1
< 0.1%
158 1
< 0.1%
156 1
< 0.1%
154 1
< 0.1%
149 2
< 0.1%
146 1
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1305
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1491.9401
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:25.111294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile727
Q11105
median1516
Q31920
95-th percentile2255
Maximum2359
Range2358
Interquartile range (IQR)815

Descriptive statistics

Standard deviation512.24471
Coefficient of variation (CV)0.34334134
Kurtosis-0.52391417
Mean1491.9401
Median Absolute Deviation (MAD)408
Skewness-0.25743788
Sum8.494182 × 108
Variance262394.64
MonotonicityNot monotonic
2024-03-30T02:56:25.437857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3201
 
0.6%
1200 1799
 
0.3%
2000 1786
 
0.3%
2100 1680
 
0.3%
950 1637
 
0.3%
1500 1618
 
0.3%
1620 1614
 
0.3%
1030 1611
 
0.3%
2025 1609
 
0.3%
1000 1603
 
0.3%
Other values (1295) 551180
96.8%
ValueCountFrequency (%)
1 165
 
< 0.1%
2 111
 
< 0.1%
3 62
 
< 0.1%
4 112
 
< 0.1%
5 458
0.1%
6 92
 
< 0.1%
7 61
 
< 0.1%
8 92
 
< 0.1%
9 76
 
< 0.1%
10 291
0.1%
ValueCountFrequency (%)
2359 3201
0.6%
2358 1069
 
0.2%
2357 640
 
0.1%
2356 520
 
0.1%
2355 904
 
0.2%
2354 351
 
0.1%
2353 413
 
0.1%
2352 347
 
0.1%
2351 282
 
< 0.1%
2350 582
 
0.1%

ARR_TIME
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.3%
Missing7145
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean1463.1941
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:25.748888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile652
Q11047
median1501
Q31915
95-th percentile2251
Maximum2400
Range2399
Interquartile range (IQR)868

Descriptive statistics

Standard deviation536.73604
Coefficient of variation (CV)0.36682492
Kurtosis-0.38786529
Mean1463.1941
Median Absolute Deviation (MAD)425
Skewness-0.34605965
Sum8.2259745 × 108
Variance288085.57
MonotonicityNot monotonic
2024-03-30T02:56:26.133095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1628 660
 
0.1%
941 649
 
0.1%
1635 642
 
0.1%
1140 642
 
0.1%
1144 641
 
0.1%
1638 638
 
0.1%
1853 632
 
0.1%
1243 632
 
0.1%
1227 627
 
0.1%
943 623
 
0.1%
Other values (1430) 555807
97.6%
(Missing) 7145
 
1.3%
ValueCountFrequency (%)
1 378
0.1%
2 317
0.1%
3 302
0.1%
4 308
0.1%
5 294
0.1%
6 300
0.1%
7 269
< 0.1%
8 268
< 0.1%
9 288
0.1%
10 262
< 0.1%
ValueCountFrequency (%)
2400 296
0.1%
2359 340
0.1%
2358 373
0.1%
2357 328
0.1%
2356 355
0.1%
2355 353
0.1%
2354 352
0.1%
2353 387
0.1%
2352 376
0.1%
2351 405
0.1%

ARR_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1148
Distinct (%)0.2%
Missing8451
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean5.3939742
Minimum-75
Maximum2367
Zeros10503
Zeros (%)1.8%
Negative357897
Negative (%)62.9%
Memory size4.3 MiB
2024-03-30T02:56:26.462474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-75
5-th percentile-26
Q1-15
median-6
Q37
95-th percentile72
Maximum2367
Range2442
Interquartile range (IQR)22

Descriptive statistics

Standard deviation55.367715
Coefficient of variation (CV)10.264735
Kurtosis187.3427
Mean5.3939742
Median Absolute Deviation (MAD)10
Skewness10.355198
Sum3025410
Variance3065.5839
MonotonicityNot monotonic
2024-03-30T02:56:26.774463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 16918
 
3.0%
-11 16858
 
3.0%
-12 16781
 
2.9%
-13 16599
 
2.9%
-9 16258
 
2.9%
-14 16170
 
2.8%
-8 16170
 
2.8%
-7 15512
 
2.7%
-15 15385
 
2.7%
-6 14833
 
2.6%
Other values (1138) 399403
70.2%
ValueCountFrequency (%)
-75 1
 
< 0.1%
-74 2
 
< 0.1%
-71 1
 
< 0.1%
-70 1
 
< 0.1%
-67 2
 
< 0.1%
-66 3
< 0.1%
-65 3
< 0.1%
-64 2
 
< 0.1%
-63 6
< 0.1%
-62 3
< 0.1%
ValueCountFrequency (%)
2367 1
< 0.1%
2338 1
< 0.1%
2280 1
< 0.1%
2275 1
< 0.1%
2204 1
< 0.1%
2029 1
< 0.1%
1771 1
< 0.1%
1751 1
< 0.1%
1705 1
< 0.1%
1693 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0.0
562280 
1.0
 
7058

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1708014
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 562280
98.8%
1.0 7058
 
1.2%

Length

2024-03-30T02:56:27.062358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:56:27.274742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 562280
98.8%
1.0 7058
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1131618
66.3%
. 569338
33.3%
1 7058
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1131618
66.3%
. 569338
33.3%
1 7058
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1131618
66.3%
. 569338
33.3%
1 7058
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1131618
66.3%
. 569338
33.3%
1 7058
 
0.4%

CANCELLATION_CODE
Categorical

MISSING 

Distinct4
Distinct (%)0.1%
Missing562280
Missing (%)98.8%
Memory size4.3 MiB
C
2981 
B
2612 
A
1463 
D
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7058
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
C 2981
 
0.5%
B 2612
 
0.5%
A 1463
 
0.3%
D 2
 
< 0.1%
(Missing) 562280
98.8%

Length

2024-03-30T02:56:27.501262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:56:27.768442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
c 2981
42.2%
b 2612
37.0%
a 1463
20.7%
d 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 2981
42.2%
B 2612
37.0%
A 1463
20.7%
D 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2981
42.2%
B 2612
37.0%
A 1463
20.7%
D 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2981
42.2%
B 2612
37.0%
A 1463
20.7%
D 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2981
42.2%
B 2612
37.0%
A 1463
20.7%
D 2
 
< 0.1%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0.0
567945 
1.0
 
1393

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1708014
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 567945
99.8%
1.0 1393
 
0.2%

Length

2024-03-30T02:56:28.281753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:56:28.499269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 567945
99.8%
1.0 1393
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1137283
66.6%
. 569338
33.3%
1 1393
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1137283
66.6%
. 569338
33.3%
1 1393
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1137283
66.6%
. 569338
33.3%
1 1393
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1708014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1137283
66.6%
. 569338
33.3%
1 1393
 
0.1%

AIR_TIME
Real number (ℝ)

MISSING 

Distinct596
Distinct (%)0.1%
Missing8451
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean112.89491
Minimum8
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:28.747886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile35
Q162
median95
Q3141
95-th percentile269.7
Maximum650
Range642
Interquartile range (IQR)79

Descriptive statistics

Standard deviation69.668573
Coefficient of variation (CV)0.61710997
Kurtosis2.1563815
Mean112.89491
Median Absolute Deviation (MAD)38
Skewness1.3823404
Sum63321286
Variance4853.71
MonotonicityNot monotonic
2024-03-30T02:56:29.206651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 5007
 
0.9%
61 4942
 
0.9%
63 4918
 
0.9%
62 4913
 
0.9%
65 4903
 
0.9%
60 4863
 
0.9%
59 4765
 
0.8%
66 4698
 
0.8%
57 4560
 
0.8%
53 4559
 
0.8%
Other values (586) 512759
90.1%
(Missing) 8451
 
1.5%
ValueCountFrequency (%)
8 5
 
< 0.1%
9 20
 
< 0.1%
10 18
 
< 0.1%
11 3
 
< 0.1%
12 6
 
< 0.1%
14 16
 
< 0.1%
15 20
 
< 0.1%
16 105
 
< 0.1%
17 237
< 0.1%
18 330
0.1%
ValueCountFrequency (%)
650 1
 
< 0.1%
640 1
 
< 0.1%
636 2
< 0.1%
635 1
 
< 0.1%
629 1
 
< 0.1%
627 1
 
< 0.1%
626 1
 
< 0.1%
624 1
 
< 0.1%
623 1
 
< 0.1%
622 3
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct895
Distinct (%)0.9%
Missing464639
Missing (%)81.6%
Infinite0
Infinite (%)0.0%
Mean25.57522
Minimum0
Maximum2360
Zeros48222
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:29.520733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q323
95-th percentile106
Maximum2360
Range2360
Interquartile range (IQR)23

Descriptive statistics

Standard deviation77.653248
Coefficient of variation (CV)3.036269
Kurtosis123.31017
Mean25.57522
Median Absolute Deviation (MAD)3
Skewness9.2562768
Sum2677700
Variance6030.027
MonotonicityNot monotonic
2024-03-30T02:56:29.825174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48222
 
8.5%
1 1826
 
0.3%
2 1710
 
0.3%
15 1704
 
0.3%
6 1669
 
0.3%
3 1651
 
0.3%
4 1636
 
0.3%
7 1530
 
0.3%
16 1489
 
0.3%
5 1466
 
0.3%
Other values (885) 41796
 
7.3%
(Missing) 464639
81.6%
ValueCountFrequency (%)
0 48222
8.5%
1 1826
 
0.3%
2 1710
 
0.3%
3 1651
 
0.3%
4 1636
 
0.3%
5 1466
 
0.3%
6 1669
 
0.3%
7 1530
 
0.3%
8 1412
 
0.2%
9 1344
 
0.2%
ValueCountFrequency (%)
2360 1
< 0.1%
2280 1
< 0.1%
2275 1
< 0.1%
1691 1
< 0.1%
1677 1
< 0.1%
1638 1
< 0.1%
1591 1
< 0.1%
1550 1
< 0.1%
1534 1
< 0.1%
1501 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct441
Distinct (%)0.4%
Missing464639
Missing (%)81.6%
Infinite0
Infinite (%)0.0%
Mean3.5982388
Minimum0
Maximum1209
Zeros99278
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:30.194675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum1209
Range1209
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.964834
Coefficient of variation (CV)8.0497253
Kurtosis461.58311
Mean3.5982388
Median Absolute Deviation (MAD)0
Skewness17.952111
Sum376732
Variance838.96159
MonotonicityNot monotonic
2024-03-30T02:56:30.553633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99278
 
17.4%
15 118
 
< 0.1%
16 118
 
< 0.1%
19 104
 
< 0.1%
8 102
 
< 0.1%
18 100
 
< 0.1%
23 90
 
< 0.1%
17 89
 
< 0.1%
11 86
 
< 0.1%
20 83
 
< 0.1%
Other values (431) 4531
 
0.8%
(Missing) 464639
81.6%
ValueCountFrequency (%)
0 99278
17.4%
1 67
 
< 0.1%
2 79
 
< 0.1%
3 80
 
< 0.1%
4 73
 
< 0.1%
5 62
 
< 0.1%
6 82
 
< 0.1%
7 71
 
< 0.1%
8 102
 
< 0.1%
9 74
 
< 0.1%
ValueCountFrequency (%)
1209 1
< 0.1%
1125 1
< 0.1%
1112 1
< 0.1%
1109 1
< 0.1%
1105 1
< 0.1%
1079 1
< 0.1%
1056 1
< 0.1%
1039 1
< 0.1%
1020 1
< 0.1%
1017 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct452
Distinct (%)0.4%
Missing464639
Missing (%)81.6%
Infinite0
Infinite (%)0.0%
Mean13.98638
Minimum0
Maximum1382
Zeros54825
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:30.852231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile61
Maximum1382
Range1382
Interquartile range (IQR)17

Descriptive statistics

Standard deviation37.400842
Coefficient of variation (CV)2.6740902
Kurtosis225.79833
Mean13.98638
Median Absolute Deviation (MAD)0
Skewness11.108494
Sum1464360
Variance1398.823
MonotonicityNot monotonic
2024-03-30T02:56:31.169967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54825
 
9.6%
1 2609
 
0.5%
2 1924
 
0.3%
3 1853
 
0.3%
15 1819
 
0.3%
16 1741
 
0.3%
4 1736
 
0.3%
5 1560
 
0.3%
17 1559
 
0.3%
6 1506
 
0.3%
Other values (442) 33567
 
5.9%
(Missing) 464639
81.6%
ValueCountFrequency (%)
0 54825
9.6%
1 2609
 
0.5%
2 1924
 
0.3%
3 1853
 
0.3%
4 1736
 
0.3%
5 1560
 
0.3%
6 1506
 
0.3%
7 1351
 
0.2%
8 1323
 
0.2%
9 1258
 
0.2%
ValueCountFrequency (%)
1382 1
< 0.1%
1246 1
< 0.1%
1138 1
< 0.1%
1135 1
< 0.1%
1105 1
< 0.1%
1104 1
< 0.1%
1092 1
< 0.1%
1089 1
< 0.1%
1055 1
< 0.1%
1022 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct87
Distinct (%)0.1%
Missing464639
Missing (%)81.6%
Infinite0
Infinite (%)0.0%
Mean0.13765174
Minimum0
Maximum600
Zeros104179
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:31.511319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum600
Range600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4992708
Coefficient of variation (CV)25.421188
Kurtosis12346.781
Mean0.13765174
Median Absolute Deviation (MAD)0
Skewness89.294279
Sum14412
Variance12.244896
MonotonicityNot monotonic
2024-03-30T02:56:31.818068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104179
 
18.3%
10 20
 
< 0.1%
15 19
 
< 0.1%
5 18
 
< 0.1%
21 16
 
< 0.1%
16 16
 
< 0.1%
19 16
 
< 0.1%
9 15
 
< 0.1%
11 15
 
< 0.1%
13 15
 
< 0.1%
Other values (77) 370
 
0.1%
(Missing) 464639
81.6%
ValueCountFrequency (%)
0 104179
18.3%
1 7
 
< 0.1%
2 8
 
< 0.1%
3 13
 
< 0.1%
4 14
 
< 0.1%
5 18
 
< 0.1%
6 7
 
< 0.1%
7 13
 
< 0.1%
8 8
 
< 0.1%
9 15
 
< 0.1%
ValueCountFrequency (%)
600 1
< 0.1%
449 1
< 0.1%
373 1
< 0.1%
191 1
< 0.1%
151 1
< 0.1%
142 1
< 0.1%
129 1
< 0.1%
115 1
< 0.1%
109 1
< 0.1%
108 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct652
Distinct (%)0.6%
Missing464639
Missing (%)81.6%
Infinite0
Infinite (%)0.0%
Mean26.878652
Minimum0
Maximum2329
Zeros52932
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:56:32.168275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q330
95-th percentile120
Maximum2329
Range2329
Interquartile range (IQR)30

Descriptive statistics

Standard deviation62.205062
Coefficient of variation (CV)2.3142925
Kurtosis127.87143
Mean26.878652
Median Absolute Deviation (MAD)0
Skewness7.9789338
Sum2814168
Variance3869.4697
MonotonicityNot monotonic
2024-03-30T02:56:32.450611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52932
 
9.3%
15 1307
 
0.2%
16 1218
 
0.2%
17 1188
 
0.2%
18 1108
 
0.2%
20 1039
 
0.2%
19 1019
 
0.2%
21 938
 
0.2%
22 914
 
0.2%
12 896
 
0.2%
Other values (642) 42140
 
7.4%
(Missing) 464639
81.6%
ValueCountFrequency (%)
0 52932
9.3%
1 679
 
0.1%
2 716
 
0.1%
3 713
 
0.1%
4 710
 
0.1%
5 734
 
0.1%
6 778
 
0.1%
7 795
 
0.1%
8 741
 
0.1%
9 757
 
0.1%
ValueCountFrequency (%)
2329 1
< 0.1%
2204 1
< 0.1%
1761 1
< 0.1%
1733 1
< 0.1%
1682 1
< 0.1%
1522 1
< 0.1%
1498 1
< 0.1%
1442 1
< 0.1%
1432 1
< 0.1%
1397 1
< 0.1%

Interactions

2024-03-30T02:55:50.643921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:47.933952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:54.354937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:00.711668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:07.806949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:14.222084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:20.621897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:26.980834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:33.817451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:40.838202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:47.607656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:55.188112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:01.989789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:08.843472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:15.607610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:22.378515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:28.840466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:33.990329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:40.231204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:45.556070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:50.928609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:48.466903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:54.653361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:01.046557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:08.178404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:14.551917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:20.950671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:27.307929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:34.190515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:41.201170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:47.958316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:55.545710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:02.349067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:09.203457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:15.939418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:22.737781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:29.085125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:34.323009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:40.508150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:45.806603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:51.188625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:48.788356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:54.989646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:01.377762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:08.496738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:14.902244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:21.244557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:27.632670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:34.513748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:41.527377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:48.332878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:55.904098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:02.685470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:09.572469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:16.295231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:23.069074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:29.323652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:34.564957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:40.767061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:46.200697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:51.495884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:49.127695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:55.337286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:01.719521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:08.822503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:15.237591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:21.622768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:28.426607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:34.870975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:41.999434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:48.672744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:56.319879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:03.010746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:09.966478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:16.631504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:23.423246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:29.587986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:34.883809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:41.054621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:46.451280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:51.751619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:49.428979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:55.641823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:02.072104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:09.147981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:15.540093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:21.923679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:28.764156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:35.216843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:42.615158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:49.012882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:56.665597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:03.302508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:10.510761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:16.931309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:23.770149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:29.803853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:35.176212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:41.310415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:46.695090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:52.030189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:49.756455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:55.995725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:02.407189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:09.496340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:15.869923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:22.296802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:29.100052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:35.574614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:43.035975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:49.314575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:57.038922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:03.647494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:10.855102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:17.229581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:24.149293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:30.258067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:35.480833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:41.597868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:46.959449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:52.290038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:50.100184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:56.356507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:02.734425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:09.829553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:16.258924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:22.621572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:29.397417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:35.887562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:43.332003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:49.671760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:57.384714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:03.962324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:11.199720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:17.547842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:24.484915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:30.485021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:35.761895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:41.838607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:47.197689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:52.540393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:50.417941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:56.660654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:03.034007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:10.193215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:16.560278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:22.919830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:29.679493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:36.252830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:43.690491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:50.096597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:57.715177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:04.291733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:11.513688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:17.847206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:24.811937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:30.717326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:36.060677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:42.137389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:47.415797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:52.774550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:50.728936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:56.988017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:03.352735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:10.513488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:16.868914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:23.224666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T02:54:26.386919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:33.256068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:40.139205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:47.005112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:54.531055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:01.353246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:08.148064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:14.991451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:21.653764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:28.353333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:33.491507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:39.622480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:45.033822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:50.151781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:55.720154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:53:53.991942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:00.383825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:07.269713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:13.827416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:20.280670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:26.647202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:33.497310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:40.459066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:47.252402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:54:54.820336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:01.622515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:08.442984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:15.259725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:21.946943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:28.593350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:33.740136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:39.927050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:45.305168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:55:50.393449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T02:55:56.494627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:55:59.303361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
019/4/2023 12:00:00 AM9E487110693BNANashville, TNTennessee5412953LGANew York, NYNew York2219511956.05.022.05.023162317.01.00.0NaN0.0114.0NaNNaNNaNNaNNaN
119/4/2023 12:00:00 AM9E487112953LGANew York, NYNew York2210693BNANashville, TNTennessee5416551759.064.031.012.018361926.050.00.0NaN0.0104.09.00.00.00.041.0
219/4/2023 12:00:00 AM9E490014524RICRichmond, VAVirginia3813487MSPMinneapolis, MNMinnesota63619614.0-5.022.010.0819807.0-12.00.0NaN0.0141.0NaNNaNNaNNaNNaN
319/4/2023 12:00:00 AM9E490111057CLTCharlotte, NCNorth Carolina3612478JFKNew York, NYNew York2219551943.0-12.015.013.022102143.0-27.00.0NaN0.092.0NaNNaNNaNNaNNaN
419/4/2023 12:00:00 AM9E490112478JFKNew York, NYNew York2211057CLTCharlotte, NCNorth Carolina3616291628.0-1.049.011.019071845.0-22.00.0NaN0.077.0NaNNaNNaNNaNNaN
519/4/2023 12:00:00 AM9E490215096SYRSyracuse, NYNew York2211433DTWDetroit, MIMichigan43615614.0-1.012.08.0746733.0-13.00.0NaN0.059.0NaNNaNNaNNaNNaN
619/4/2023 12:00:00 AM9E490310599BHMBirmingham, ALAlabama5112953LGANew York, NYNew York2212361232.0-4.013.06.016041558.0-6.00.0NaN0.0127.0NaNNaNNaNNaNNaN
719/4/2023 12:00:00 AM9E490312953LGANew York, NYNew York2210599BHMBirmingham, ALAlabama51841836.0-5.022.03.01022959.0-23.00.0NaN0.0118.0NaNNaNNaNNaNNaN
819/4/2023 12:00:00 AM9E490410581BGRBangor, MEMaine1212953LGANew York, NYNew York2212211216.0-5.010.05.014041337.0-27.00.0NaN0.066.0NaNNaNNaNNaNNaN
919/4/2023 12:00:00 AM9E490412953LGANew York, NYNew York2210581BGRBangor, MEMaine12945931.0-14.028.03.011341102.0-32.00.0NaN0.060.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
56932879/24/2023 12:00:00 AMYX582011057CLTCharlotte, NCNorth Carolina3612478JFKNew York, NYNew York22630626.0-4.018.017.0838815.0-23.00.0NaN0.074.0NaNNaNNaNNaNNaN
56932979/24/2023 12:00:00 AMYX582311057CLTCharlotte, NCNorth Carolina3612953LGANew York, NYNew York2219271926.0-1.027.013.021292123.0-6.00.0NaN0.077.0NaNNaNNaNNaNNaN
56933079/24/2023 12:00:00 AMYX582412478JFKNew York, NYNew York2211057CLTCharlotte, NCNorth Carolina3620002103.063.038.08.022222308.046.00.0NaN0.079.00.00.00.00.046.0
56933179/24/2023 12:00:00 AMYX582611433DTWDetroit, MIMichigan4314122PITPittsburgh, PAPennsylvania2321202124.04.012.05.022222215.0-7.00.0NaN0.034.0NaNNaNNaNNaNNaN
56933279/24/2023 12:00:00 AMYX582812339INDIndianapolis, INIndiana4212953LGANew York, NYNew York2214451525.040.019.016.016541740.046.00.0NaN0.0100.00.00.046.00.00.0
56933379/24/2023 12:00:00 AMYX582812953LGANew York, NYNew York2212339INDIndianapolis, INIndiana4211301121.0-9.031.08.013521334.0-18.00.0NaN0.094.0NaNNaNNaNNaNNaN
56933479/24/2023 12:00:00 AMYX583410721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3815151538.023.051.044.017081833.085.00.0NaN0.080.00.00.062.00.023.0
56933579/24/2023 12:00:00 AMYX583411278DCAWashington, DCVirginia3810721BOSBoston, MAMassachusetts1318301948.078.046.023.020182158.0100.00.0NaN0.061.00.00.054.00.046.0
56933679/24/2023 12:00:00 AMYX583614524RICRichmond, VAVirginia3812953LGANew York, NYNew York22900854.0-6.015.06.010291000.0-29.00.0NaN0.045.0NaNNaNNaNNaNNaN
56933779/24/2023 12:00:00 AMYX583912953LGANew York, NYNew York2213485MSNMadison, WIWisconsin4520502129.039.019.05.022272245.018.00.0NaN0.0112.00.00.00.00.018.0